Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations46713
Missing cells0
Missing cells (%)0.0%
Duplicate rows35
Duplicate rows (%)0.1%
Total size in memory6.1 MiB
Average record size in memory136.0 B

Variable types

Categorical4
Text1
Numeric11
DateTime1

Alerts

Dataset has 35 (0.1%) duplicate rowsDuplicates
Bdrms is highly overall correlated with Fbath and 2 other fieldsHigh correlation
District is highly overall correlated with NbhdHigh correlation
Fbath is highly overall correlated with Bdrms and 2 other fieldsHigh correlation
Fin_sqft is highly overall correlated with Bdrms and 4 other fieldsHigh correlation
Lotsize is highly overall correlated with Year_BuiltHigh correlation
Nbhd is highly overall correlated with District and 2 other fieldsHigh correlation
Nr_of_rms is highly overall correlated with Sale_priceHigh correlation
PropType is highly overall correlated with Nbhd and 1 other fieldsHigh correlation
Sale_price is highly overall correlated with Nr_of_rmsHigh correlation
Stories is highly overall correlated with Fin_sqft and 2 other fieldsHigh correlation
Style is highly overall correlated with Fin_sqft and 4 other fieldsHigh correlation
Units is highly overall correlated with Bdrms and 4 other fieldsHigh correlation
Year_Built is highly overall correlated with LotsizeHigh correlation
PropType is highly imbalanced (97.8%)Imbalance
Hbath is highly imbalanced (54.9%)Imbalance
Fin_sqft is highly skewed (γ1 = 27.23746815)Skewed
Units is highly skewed (γ1 = 160.0138676)Skewed
Bdrms is highly skewed (γ1 = 209.8119759)Skewed
Nr_of_rms has 24929 (53.4%) zerosZeros

Reproduction

Analysis started2024-08-04 08:05:56.613017
Analysis finished2024-08-04 08:06:44.918019
Duration48.31 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

PropType
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size365.1 KiB
Residential
46493 
Commercial
 
215
Exempt
 
4
Condominium
 
1

Length

Max length11
Median length11
Mean length10.994969
Min length6

Characters and Unicode

Total characters513608
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowResidential
2nd rowResidential
3rd rowResidential
4th rowResidential
5th rowResidential

Common Values

ValueCountFrequency (%)
Residential 46493
99.5%
Commercial 215
 
0.5%
Exempt 4
 
< 0.1%
Condominium 1
 
< 0.1%

Length

2024-08-04T10:06:45.166003image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-04T10:06:45.735774image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
residential 46493
99.5%
commercial 215
 
0.5%
exempt 4
 
< 0.1%
condominium 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 93205
18.1%
i 93203
18.1%
l 46708
9.1%
a 46708
9.1%
t 46497
9.1%
n 46495
9.1%
d 46494
9.1%
R 46493
9.1%
s 46493
9.1%
m 436
 
0.1%
Other values (8) 876
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 466895
90.9%
Uppercase Letter 46713
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 93205
20.0%
i 93203
20.0%
l 46708
10.0%
a 46708
10.0%
t 46497
10.0%
n 46495
10.0%
d 46494
10.0%
s 46493
10.0%
m 436
 
0.1%
o 217
 
< 0.1%
Other values (5) 439
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
R 46493
99.5%
C 216
 
0.5%
E 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 513608
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 93205
18.1%
i 93203
18.1%
l 46708
9.1%
a 46708
9.1%
t 46497
9.1%
n 46495
9.1%
d 46494
9.1%
R 46493
9.1%
s 46493
9.1%
m 436
 
0.1%
Other values (8) 876
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 93205
18.1%
i 93203
18.1%
l 46708
9.1%
a 46708
9.1%
t 46497
9.1%
n 46495
9.1%
d 46494
9.1%
R 46493
9.1%
s 46493
9.1%
m 436
 
0.1%
Other values (8) 876
 
0.2%
Distinct38081
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Memory size365.1 KiB
2024-08-04T10:06:46.661596image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length35
Median length32
Mean length15.57727
Min length12

Characters and Unicode

Total characters727661
Distinct characters46
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30673 ?
Unique (%)65.7%

Sample

1st row3033 N 35TH ST
2nd row1908 E WEBSTER PL
3rd row812 N 25TH ST
4th row959 N 34TH ST
5th row3209 W WELLS ST
ValueCountFrequency (%)
st 30817
 
16.4%
n 20541
 
10.9%
s 13247
 
7.0%
w 10853
 
5.8%
av 10730
 
5.7%
e 2093
 
1.1%
pl 1739
 
0.9%
dr 978
 
0.5%
bl 778
 
0.4%
ct 673
 
0.4%
Other values (10704) 95912
50.9%
2024-08-04T10:06:47.648321image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
141658
19.5%
T 61198
 
8.4%
S 51646
 
7.1%
3 35044
 
4.8%
N 34652
 
4.8%
2 34564
 
4.8%
1 30139
 
4.1%
4 26540
 
3.6%
5 25512
 
3.5%
H 24901
 
3.4%
Other values (36) 261807
36.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 334283
45.9%
Decimal Number 247926
34.1%
Space Separator 141658
19.5%
Dash Punctuation 3708
 
0.5%
Lowercase Letter 54
 
< 0.1%
Other Punctuation 25
 
< 0.1%
Close Punctuation 4
 
< 0.1%
Modifier Symbol 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 61198
18.3%
S 51646
15.4%
N 34652
10.4%
H 24901
 
7.4%
A 24430
 
7.3%
E 17510
 
5.2%
R 16397
 
4.9%
W 14271
 
4.3%
L 13440
 
4.0%
V 12596
 
3.8%
Other values (16) 63242
18.9%
Decimal Number
ValueCountFrequency (%)
3 35044
14.1%
2 34564
13.9%
1 30139
12.2%
4 26540
10.7%
5 25512
10.3%
0 21327
8.6%
6 20486
8.3%
7 19876
8.0%
8 18513
7.5%
9 15925
6.4%
Other Punctuation
ValueCountFrequency (%)
, 19
76.0%
\ 5
 
20.0%
. 1
 
4.0%
Lowercase Letter
ValueCountFrequency (%)
n 18
33.3%
i 18
33.3%
t 18
33.3%
Space Separator
ValueCountFrequency (%)
141658
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3708
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 393324
54.1%
Latin 334337
45.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 61198
18.3%
S 51646
15.4%
N 34652
10.4%
H 24901
 
7.4%
A 24430
 
7.3%
E 17510
 
5.2%
R 16397
 
4.9%
W 14271
 
4.3%
L 13440
 
4.0%
V 12596
 
3.8%
Other values (19) 63296
18.9%
Common
ValueCountFrequency (%)
141658
36.0%
3 35044
 
8.9%
2 34564
 
8.8%
1 30139
 
7.7%
4 26540
 
6.7%
5 25512
 
6.5%
0 21327
 
5.4%
6 20486
 
5.2%
7 19876
 
5.1%
8 18513
 
4.7%
Other values (7) 19665
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 727661
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
141658
19.5%
T 61198
 
8.4%
S 51646
 
7.1%
3 35044
 
4.8%
N 34652
 
4.8%
2 34564
 
4.8%
1 30139
 
4.1%
4 26540
 
3.6%
5 25512
 
3.5%
H 24901
 
3.4%
Other values (36) 261807
36.0%

District
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.411256
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size365.1 KiB
2024-08-04T10:06:47.948256image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q312
95-th percentile14
Maximum15
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2262715
Coefficient of variation (CV)0.50245428
Kurtosis-1.1947322
Mean8.411256
Median Absolute Deviation (MAD)4
Skewness-0.21335309
Sum392915
Variance17.861371
MonotonicityNot monotonic
2024-08-04T10:06:48.218481image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
5 6355
13.6%
11 5874
12.6%
14 4893
10.5%
10 4844
10.4%
13 4560
9.8%
2 3216
6.9%
7 2809
6.0%
3 2651
5.7%
1 2644
5.7%
9 2346
 
5.0%
Other values (5) 6521
14.0%
ValueCountFrequency (%)
1 2644
5.7%
2 3216
6.9%
3 2651
5.7%
4 340
 
0.7%
5 6355
13.6%
6 1804
 
3.9%
7 2809
6.0%
8 1697
 
3.6%
9 2346
 
5.0%
10 4844
10.4%
ValueCountFrequency (%)
15 1526
 
3.3%
14 4893
10.5%
13 4560
9.8%
12 1154
 
2.5%
11 5874
12.6%
10 4844
10.4%
9 2346
 
5.0%
8 1697
 
3.6%
7 2809
6.0%
6 1804
 
3.9%

Nbhd
Real number (ℝ)

HIGH CORRELATION 

Distinct184
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2901.9262
Minimum40
Maximum6470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size365.1 KiB
2024-08-04T10:06:48.547394image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile560
Q11740
median2840
Q34340
95-th percentile4780
Maximum6470
Range6430
Interquartile range (IQR)2600

Descriptive statistics

Standard deviation1430.9731
Coefficient of variation (CV)0.49311148
Kurtosis-1.2601556
Mean2901.9262
Median Absolute Deviation (MAD)1400
Skewness-0.091377298
Sum1.3555768 × 108
Variance2047684.1
MonotonicityNot monotonic
2024-08-04T10:06:48.894304image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2100 1539
 
3.3%
2080 1349
 
2.9%
4520 1162
 
2.5%
4340 1144
 
2.4%
4240 1087
 
2.3%
4420 1069
 
2.3%
4620 907
 
1.9%
4580 788
 
1.7%
4700 760
 
1.6%
2040 752
 
1.6%
Other values (174) 36156
77.4%
ValueCountFrequency (%)
40 146
 
0.3%
50 65
 
0.1%
240 537
1.1%
360 247
 
0.5%
380 100
 
0.2%
440 338
0.7%
480 696
1.5%
520 73
 
0.2%
560 310
0.7%
600 193
 
0.4%
ValueCountFrequency (%)
6470 1
 
< 0.1%
6465 2
 
< 0.1%
6460 10
< 0.1%
6423 1
 
< 0.1%
6290 3
 
< 0.1%
6288 4
 
< 0.1%
6286 2
 
< 0.1%
6284 16
< 0.1%
6283 8
< 0.1%
6282 7
< 0.1%

Style
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size365.1 KiB
Ranch
13374 
Cape Cod
8957 
Milwaukee Bungalow
3720 
Duplex O/S
3447 
Residence O/S
2926 
Other values (36)
14289 

Length

Max length50
Median length42
Mean length9.4530645
Min length4

Characters and Unicode

Total characters441581
Distinct characters50
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowAP 1
2nd rowRm or Rooming House
3rd rowRm or Rooming House
4th rowAP 1
5th rowMansion

Common Values

ValueCountFrequency (%)
Ranch 13374
28.6%
Cape Cod 8957
19.2%
Milwaukee Bungalow 3720
 
8.0%
Duplex O/S 3447
 
7.4%
Residence O/S 2926
 
6.3%
Colonial 2884
 
6.2%
Dplx Bungalow 2692
 
5.8%
Duplex N/S 2405
 
5.1%
Res O/S A & 1/2 1705
 
3.6%
Cottage 1026
 
2.2%
Other values (31) 3577
 
7.7%

Length

2024-08-04T10:06:49.280362image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ranch 13375
16.3%
cape 8957
10.9%
cod 8957
10.9%
o/s 8770
10.7%
bungalow 6412
 
7.8%
duplex 5852
 
7.1%
milwaukee 3720
 
4.5%
residence 3247
 
4.0%
colonial 2884
 
3.5%
dplx 2692
 
3.3%
Other values (61) 17333
21.1%

Most occurring characters

ValueCountFrequency (%)
e 39000
 
8.8%
a 37181
 
8.4%
35486
 
8.0%
l 27203
 
6.2%
n 27076
 
6.1%
o 25406
 
5.8%
C 22251
 
5.0%
R 19238
 
4.4%
p 19036
 
4.3%
u 17846
 
4.0%
Other values (40) 171858
38.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 296617
67.2%
Uppercase Letter 88892
 
20.1%
Space Separator 35486
 
8.0%
Other Punctuation 15027
 
3.4%
Decimal Number 4130
 
0.9%
Dash Punctuation 672
 
0.2%
Math Symbol 593
 
0.1%
Open Punctuation 154
 
< 0.1%
Close Punctuation 10
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 39000
13.1%
a 37181
12.5%
l 27203
9.2%
n 27076
9.1%
o 25406
8.6%
p 19036
 
6.4%
u 17846
 
6.0%
c 16798
 
5.7%
h 13897
 
4.7%
d 13222
 
4.5%
Other values (12) 59952
20.2%
Uppercase Letter
ValueCountFrequency (%)
C 22251
25.0%
R 19238
21.6%
S 12116
13.6%
D 8947
10.1%
O 8933
10.0%
B 6916
 
7.8%
M 4203
 
4.7%
N 2405
 
2.7%
A 1886
 
2.1%
T 1428
 
1.6%
Other values (6) 569
 
0.6%
Decimal Number
ValueCountFrequency (%)
2 2298
55.6%
1 1830
44.3%
4 1
 
< 0.1%
6 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 12880
85.7%
& 2002
 
13.3%
, 145
 
1.0%
Space Separator
ValueCountFrequency (%)
35486
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 672
100.0%
Math Symbol
ValueCountFrequency (%)
+ 593
100.0%
Open Punctuation
ValueCountFrequency (%)
( 154
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 385509
87.3%
Common 56072
 
12.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 39000
 
10.1%
a 37181
 
9.6%
l 27203
 
7.1%
n 27076
 
7.0%
o 25406
 
6.6%
C 22251
 
5.8%
R 19238
 
5.0%
p 19036
 
4.9%
u 17846
 
4.6%
c 16798
 
4.4%
Other values (28) 134474
34.9%
Common
ValueCountFrequency (%)
35486
63.3%
/ 12880
 
23.0%
2 2298
 
4.1%
& 2002
 
3.6%
1 1830
 
3.3%
- 672
 
1.2%
+ 593
 
1.1%
( 154
 
0.3%
, 145
 
0.3%
) 10
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 441581
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 39000
 
8.8%
a 37181
 
8.4%
35486
 
8.0%
l 27203
 
6.2%
n 27076
 
6.1%
o 25406
 
5.8%
C 22251
 
5.0%
R 19238
 
4.4%
p 19036
 
4.3%
u 17846
 
4.0%
Other values (40) 171858
38.9%

Extwall
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size365.1 KiB
Aluminum / Vinyl
13931 
Aluminum/Vinyl
13143 
Brick
10147 
Frame
2590 
Stone
1615 
Other values (15)
5287 

Length

Max length23
Median length17
Mean length11.380237
Min length4

Characters and Unicode

Total characters531605
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrame
2nd rowFrame
3rd rowFrame
4th rowFrame
5th rowStone

Common Values

ValueCountFrequency (%)
Aluminum / Vinyl 13931
29.8%
Aluminum/Vinyl 13143
28.1%
Brick 10147
21.7%
Frame 2590
 
5.5%
Stone 1615
 
3.5%
Asphalt/Other 1210
 
2.6%
Wood 1171
 
2.5%
Stucco 807
 
1.7%
Masonry / Frame 760
 
1.6%
Masonry/Frame 593
 
1.3%
Other values (10) 746
 
1.6%

Length

2024-08-04T10:06:49.751812image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14691
19.2%
aluminum 13931
18.2%
vinyl 13931
18.2%
aluminum/vinyl 13143
17.2%
brick 10147
13.3%
frame 3361
 
4.4%
stone 1615
 
2.1%
wood 1288
 
1.7%
asphalt/other 1210
 
1.6%
stucco 807
 
1.1%
Other values (14) 2304
 
3.0%

Most occurring characters

ValueCountFrequency (%)
i 64820
12.2%
m 58551
11.0%
n 57664
10.8%
l 55854
10.5%
u 54996
10.3%
/ 29820
 
5.6%
29715
 
5.6%
y 28523
 
5.4%
A 28325
 
5.3%
V 27115
 
5.1%
Other values (23) 96222
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 394906
74.3%
Uppercase Letter 77015
 
14.5%
Other Punctuation 29820
 
5.6%
Space Separator 29715
 
5.6%
Dash Punctuation 149
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 64820
16.4%
m 58551
14.8%
n 57664
14.6%
l 55854
14.1%
u 54996
13.9%
y 28523
7.2%
r 17256
 
4.4%
c 12045
 
3.1%
k 10556
 
2.7%
e 7816
 
2.0%
Other values (9) 26825
6.8%
Uppercase Letter
ValueCountFrequency (%)
A 28325
36.8%
V 27115
35.2%
B 10414
 
13.5%
F 4245
 
5.5%
S 2468
 
3.2%
M 1372
 
1.8%
W 1288
 
1.7%
O 1221
 
1.6%
C 305
 
0.4%
H 142
 
0.2%
Other Punctuation
ValueCountFrequency (%)
/ 29820
100.0%
Space Separator
ValueCountFrequency (%)
29715
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 149
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 471921
88.8%
Common 59684
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 64820
13.7%
m 58551
12.4%
n 57664
12.2%
l 55854
11.8%
u 54996
11.7%
y 28523
6.0%
A 28325
6.0%
V 27115
5.7%
r 17256
 
3.7%
c 12045
 
2.6%
Other values (20) 66772
14.1%
Common
ValueCountFrequency (%)
/ 29820
50.0%
29715
49.8%
- 149
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 531605
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 64820
12.2%
m 58551
11.0%
n 57664
10.8%
l 55854
10.5%
u 54996
10.3%
/ 29820
 
5.6%
29715
 
5.6%
y 28523
 
5.4%
A 28325
 
5.3%
V 27115
 
5.1%
Other values (23) 96222
18.1%

Stories
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.321463
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size365.1 KiB
2024-08-04T10:06:50.057503image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31.5
95-th percentile2
Maximum8
Range7
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.4275148
Coefficient of variation (CV)0.32351629
Kurtosis0.28388076
Mean1.321463
Median Absolute Deviation (MAD)0
Skewness0.87098293
Sum61729.5
Variance0.18276891
MonotonicityNot monotonic
2024-08-04T10:06:50.327769image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 28168
60.3%
2 11315
24.2%
1.5 7169
 
15.3%
3 32
 
0.1%
2.5 25
 
0.1%
4 2
 
< 0.1%
3.5 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
1 28168
60.3%
1.5 7169
 
15.3%
2 11315
24.2%
2.5 25
 
0.1%
3 32
 
0.1%
3.5 1
 
< 0.1%
4 2
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
4 2
 
< 0.1%
3.5 1
 
< 0.1%
3 32
 
0.1%
2.5 25
 
0.1%
2 11315
24.2%
1.5 7169
 
15.3%
1 28168
60.3%

Year_Built
Real number (ℝ)

HIGH CORRELATION 

Distinct176
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1939.5678
Minimum1835
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size365.1 KiB
2024-08-04T10:06:50.646704image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1835
5-th percentile1895
Q11923
median1948
Q31956
95-th percentile1972
Maximum2023
Range188
Interquartile range (IQR)33

Descriptive statistics

Standard deviation24.891764
Coefficient of variation (CV)0.012833665
Kurtosis0.084214438
Mean1939.5678
Median Absolute Deviation (MAD)14
Skewness-0.24112662
Sum90603031
Variance619.59992
MonotonicityNot monotonic
2024-08-04T10:06:51.039607image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1955 1906
 
4.1%
1953 1792
 
3.8%
1952 1688
 
3.6%
1950 1635
 
3.5%
1956 1615
 
3.5%
1954 1491
 
3.2%
1951 1362
 
2.9%
1957 1282
 
2.7%
1949 1151
 
2.5%
1958 1109
 
2.4%
Other values (166) 31682
67.8%
ValueCountFrequency (%)
1835 1
 
< 0.1%
1836 2
< 0.1%
1840 1
 
< 0.1%
1843 1
 
< 0.1%
1844 1
 
< 0.1%
1848 1
 
< 0.1%
1850 3
< 0.1%
1853 1
 
< 0.1%
1854 1
 
< 0.1%
1855 2
< 0.1%
ValueCountFrequency (%)
2023 1
 
< 0.1%
2022 5
 
< 0.1%
2021 2
 
< 0.1%
2020 4
 
< 0.1%
2019 4
 
< 0.1%
2018 10
< 0.1%
2017 17
< 0.1%
2016 12
< 0.1%
2015 6
 
< 0.1%
2014 9
< 0.1%

Nr_of_rms
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4222165
Minimum0
Maximum63
Zeros24929
Zeros (%)53.4%
Negative0
Negative (%)0.0%
Memory size365.1 KiB
2024-08-04T10:06:51.363023image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile11
Maximum63
Range63
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2513098
Coefficient of variation (CV)1.2422679
Kurtosis4.0848375
Mean3.4222165
Median Absolute Deviation (MAD)0
Skewness1.3359308
Sum159862
Variance18.073635
MonotonicityNot monotonic
2024-08-04T10:06:51.689452image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 24929
53.4%
5 5668
 
12.1%
6 4383
 
9.4%
10 2551
 
5.5%
7 2209
 
4.7%
8 1774
 
3.8%
4 1594
 
3.4%
9 1107
 
2.4%
12 973
 
2.1%
11 552
 
1.2%
Other values (30) 973
 
2.1%
ValueCountFrequency (%)
0 24929
53.4%
2 1
 
< 0.1%
3 39
 
0.1%
4 1594
 
3.4%
5 5668
 
12.1%
6 4383
 
9.4%
7 2209
 
4.7%
8 1774
 
3.8%
9 1107
 
2.4%
10 2551
 
5.5%
ValueCountFrequency (%)
63 1
 
< 0.1%
62 1
 
< 0.1%
45 2
< 0.1%
44 1
 
< 0.1%
40 2
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%
36 3
< 0.1%
33 1
 
< 0.1%
32 3
< 0.1%

Fin_sqft
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3180
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1547.8492
Minimum256
Maximum81865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size365.1 KiB
2024-08-04T10:06:52.031888image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum256
5-th percentile852
Q11092
median1355
Q31847
95-th percentile2729
Maximum81865
Range81609
Interquartile range (IQR)755

Descriptive statistics

Standard deviation847.89721
Coefficient of variation (CV)0.54779059
Kurtosis2162.5248
Mean1547.8492
Median Absolute Deviation (MAD)324
Skewness27.237468
Sum72304678
Variance718929.69
MonotonicityNot monotonic
2024-08-04T10:06:52.689095image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 256
 
0.5%
936 227
 
0.5%
672 170
 
0.4%
1120 162
 
0.3%
1008 156
 
0.3%
1176 140
 
0.3%
1092 131
 
0.3%
1200 129
 
0.3%
1150 123
 
0.3%
1064 116
 
0.2%
Other values (3170) 45103
96.6%
ValueCountFrequency (%)
256 1
 
< 0.1%
416 1
 
< 0.1%
452 1
 
< 0.1%
484 1
 
< 0.1%
487 1
 
< 0.1%
500 2
< 0.1%
504 4
< 0.1%
512 1
 
< 0.1%
518 1
 
< 0.1%
520 2
< 0.1%
ValueCountFrequency (%)
81865 1
< 0.1%
57137 1
< 0.1%
26930 1
< 0.1%
21000 1
< 0.1%
19477 2
< 0.1%
16080 1
< 0.1%
15904 1
< 0.1%
13596 1
< 0.1%
12775 1
< 0.1%
12411 1
< 0.1%

Units
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2694753
Minimum0
Maximum431
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size365.1 KiB
2024-08-04T10:06:53.502069image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum431
Range431
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2604908
Coefficient of variation (CV)1.7806497
Kurtosis29026.479
Mean1.2694753
Median Absolute Deviation (MAD)0
Skewness160.01387
Sum59301
Variance5.1098186
MonotonicityNot monotonic
2024-08-04T10:06:53.809244image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 36222
77.5%
2 9635
 
20.6%
3 636
 
1.4%
4 117
 
0.3%
5 28
 
0.1%
6 27
 
0.1%
7 12
 
< 0.1%
8 8
 
< 0.1%
10 5
 
< 0.1%
9 5
 
< 0.1%
Other values (12) 18
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 36222
77.5%
2 9635
 
20.6%
3 636
 
1.4%
4 117
 
0.3%
5 28
 
0.1%
6 27
 
0.1%
7 12
 
< 0.1%
8 8
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
431 1
 
< 0.1%
191 1
 
< 0.1%
34 1
 
< 0.1%
31 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
23 1
 
< 0.1%
16 1
 
< 0.1%
13 3
< 0.1%
12 2
< 0.1%

Bdrms
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5903282
Minimum0
Maximum2031
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size365.1 KiB
2024-08-04T10:06:54.260194image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile6
Maximum2031
Range2031
Interquartile range (IQR)1

Descriptive statistics

Standard deviation9.4739969
Coefficient of variation (CV)2.6387551
Kurtosis44898.466
Mean3.5903282
Median Absolute Deviation (MAD)1
Skewness209.81198
Sum167715
Variance89.756617
MonotonicityNot monotonic
2024-08-04T10:06:54.661801image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3 22344
47.8%
4 11133
23.8%
2 6002
 
12.8%
6 3268
 
7.0%
5 2699
 
5.8%
8 397
 
0.8%
7 336
 
0.7%
1 231
 
0.5%
9 88
 
0.2%
10 80
 
0.2%
Other values (15) 135
 
0.3%
ValueCountFrequency (%)
0 12
 
< 0.1%
1 231
 
0.5%
2 6002
 
12.8%
3 22344
47.8%
4 11133
23.8%
5 2699
 
5.8%
6 3268
 
7.0%
7 336
 
0.7%
8 397
 
0.8%
9 88
 
0.2%
ValueCountFrequency (%)
2031 1
 
< 0.1%
32 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
25 2
 
< 0.1%
21 1
 
< 0.1%
20 4
< 0.1%
18 6
< 0.1%
16 2
 
< 0.1%
15 6
< 0.1%

Fbath
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4840623
Minimum0
Maximum10
Zeros248
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size365.1 KiB
2024-08-04T10:06:55.051691image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6236543
Coefficient of variation (CV)0.4202346
Kurtosis2.8233064
Mean1.4840623
Median Absolute Deviation (MAD)0
Skewness1.1180416
Sum69325
Variance0.38894469
MonotonicityNot monotonic
2024-08-04T10:06:55.330058image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 26151
56.0%
2 18121
38.8%
3 1910
 
4.1%
0 248
 
0.5%
4 233
 
0.5%
5 37
 
0.1%
6 10
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 248
 
0.5%
1 26151
56.0%
2 18121
38.8%
3 1910
 
4.1%
4 233
 
0.5%
5 37
 
0.1%
6 10
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 10
 
< 0.1%
5 37
 
0.1%
4 233
 
0.5%
3 1910
 
4.1%
2 18121
38.8%
1 26151
56.0%
0 248
 
0.5%

Hbath
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size365.1 KiB
0
31527 
1
13983 
2
 
1151
3
 
51
10
 
1

Length

Max length2
Median length1
Mean length1.0000214
Min length1

Characters and Unicode

Total characters46714
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31527
67.5%
1 13983
29.9%
2 1151
 
2.5%
3 51
 
0.1%
10 1
 
< 0.1%

Length

2024-08-04T10:06:55.647483image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-04T10:06:55.915153image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 31527
67.5%
1 13983
29.9%
2 1151
 
2.5%
3 51
 
0.1%
10 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 31528
67.5%
1 13984
29.9%
2 1151
 
2.5%
3 51
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46714
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 31528
67.5%
1 13984
29.9%
2 1151
 
2.5%
3 51
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 46714
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31528
67.5%
1 13984
29.9%
2 1151
 
2.5%
3 51
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31528
67.5%
1 13984
29.9%
2 1151
 
2.5%
3 51
 
0.1%

Lotsize
Real number (ℝ)

HIGH CORRELATION 

Distinct4408
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6157.6553
Minimum0
Maximum227819
Zeros200
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size365.1 KiB
2024-08-04T10:06:56.183986image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3300
Q14730
median5400
Q37168
95-th percentile10692
Maximum227819
Range227819
Interquartile range (IQR)2438

Descriptive statistics

Standard deviation3953.8268
Coefficient of variation (CV)0.6420994
Kurtosis817.44476
Mean6157.6553
Median Absolute Deviation (MAD)1132
Skewness18.801894
Sum2.8764255 × 108
Variance15632746
MonotonicityNot monotonic
2024-08-04T10:06:56.560130image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4800 3419
 
7.3%
3600 2292
 
4.9%
6000 1272
 
2.7%
5400 1230
 
2.6%
7200 1122
 
2.4%
5000 951
 
2.0%
4920 725
 
1.6%
4200 609
 
1.3%
5040 519
 
1.1%
5160 458
 
1.0%
Other values (4398) 34116
73.0%
ValueCountFrequency (%)
0 200
0.4%
1 2
 
< 0.1%
613 1
 
< 0.1%
930 2
 
< 0.1%
1018 1
 
< 0.1%
1050 6
 
< 0.1%
1084 1
 
< 0.1%
1098 1
 
< 0.1%
1120 1
 
< 0.1%
1188 1
 
< 0.1%
ValueCountFrequency (%)
227819 1
< 0.1%
219978 2
< 0.1%
209524 1
< 0.1%
128502 1
< 0.1%
119790 1
< 0.1%
101059 1
< 0.1%
95832 1
< 0.1%
90000 1
< 0.1%
84071 1
< 0.1%
83200 1
< 0.1%
Distinct212
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size365.1 KiB
Minimum2002-02-01 00:00:00
Maximum2023-12-01 00:00:00
2024-08-04T10:06:57.094171image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:57.495503image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Sale_price
Real number (ℝ)

HIGH CORRELATION 

Distinct4475
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean610555.46
Minimum0
Maximum26250000
Zeros30
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size365.1 KiB
2024-08-04T10:06:58.286896image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51000
Q1113000
median163000
Q3488400
95-th percentile2600000
Maximum26250000
Range26250000
Interquartile range (IQR)375400

Descriptive statistics

Standard deviation984902.32
Coefficient of variation (CV)1.6131251
Kurtosis38.536111
Mean610555.46
Median Absolute Deviation (MAD)70300
Skewness3.7500625
Sum2.8520877 × 1010
Variance9.7003258 × 1011
MonotonicityNot monotonic
2024-08-04T10:06:58.803940image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150000 448
 
1.0%
125000 395
 
0.8%
140000 395
 
0.8%
135000 390
 
0.8%
130000 389
 
0.8%
110000 382
 
0.8%
120000 377
 
0.8%
160000 369
 
0.8%
115000 362
 
0.8%
165000 356
 
0.8%
Other values (4465) 42850
91.7%
ValueCountFrequency (%)
0 30
0.1%
100 1
 
< 0.1%
1000 1
 
< 0.1%
1100 1
 
< 0.1%
1250 1
 
< 0.1%
2000 1
 
< 0.1%
3500 1
 
< 0.1%
3900 1
 
< 0.1%
4000 1
 
< 0.1%
5000 9
 
< 0.1%
ValueCountFrequency (%)
26250000 1
< 0.1%
25000000 1
< 0.1%
20800000 1
< 0.1%
19190000 1
< 0.1%
16090000 1
< 0.1%
15500000 1
< 0.1%
14370000 1
< 0.1%
13400000 1
< 0.1%
13000000 1
< 0.1%
12920500 1
< 0.1%

Interactions

2024-08-04T10:06:38.689262image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:05:59.892058image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:02.996726image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:05.978124image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:09.176174image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:13.786518image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:18.041004image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:23.658229image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:28.970831image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:33.123574image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:35.810906image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:38.955158image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:00.147874image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:03.261906image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:06.258252image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:09.484750image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:14.274503image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:18.311247image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:23.881082image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:29.591862image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:33.342383image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:36.135208image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:39.417683image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:00.487129image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:03.502961image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:06.582776image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:09.775469image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:14.751053image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:18.591656image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:24.107355image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:30.190006image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:33.566184image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:36.414650image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:39.713359image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:00.688460image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:03.740853image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:06.879274image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:10.065732image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:15.270272image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:19.040730image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:24.582215image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:30.761041image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:33.814344image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:36.716258image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:39.962950image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:00.905891image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:03.977090image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:07.182799image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:10.466208image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:15.757413image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:19.812147image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:24.980469image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:31.054112image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:34.053255image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:36.971024image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:40.238516image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:01.118649image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:04.273467image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:07.467854image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:10.857577image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:15.997978image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:20.436060image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:25.556650image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:31.303207image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:34.271803image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:37.253470image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:40.577781image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:01.458488image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:04.537359image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:07.766847image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:11.468043image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:16.268585image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:21.018620image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:26.183494image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:31.651537image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:34.517043image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:37.503693image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:40.882758image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:01.655319image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:04.778907image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:08.037598image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:11.863640image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:16.490807image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:21.497808image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:26.638198image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:31.946581image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:34.716174image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:37.714361image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:41.211816image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:01.944002image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:05.066723image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:08.373671image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:12.435758image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:17.008938image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:21.979888image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:27.272920image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:32.335670image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:35.018169image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:37.942212image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:41.458631image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:02.188524image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:05.343279image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:08.632061image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:12.883561image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:17.500817image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:22.577621image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:27.906692image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:32.616868image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:35.260142image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:38.192373image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:41.718772image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:02.488007image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:05.681076image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:08.872579image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:13.301838image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:17.793668image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:23.225017image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:28.474949image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:32.881106image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:35.560804image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-04T10:06:38.435592image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-08-04T10:06:59.638202image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
BdrmsDistrictExtwallFbathFin_sqftHbathLotsizeNbhdNr_of_rmsPropTypeSale_priceStoriesStyleUnitsYear_Built
Bdrms1.000-0.0220.0000.5090.6670.000-0.084-0.0220.2940.0000.1750.5000.0100.534-0.170
District-0.0221.0000.1090.0090.0000.111-0.0780.728-0.0420.0240.0740.0170.2600.021-0.100
Extwall0.0000.1091.0000.0640.3060.0910.0470.2140.1810.3310.0860.2870.3070.4490.199
Fbath0.5090.0090.0641.0000.6110.151-0.0860.0290.1990.0320.1730.4760.3120.563-0.187
Fin_sqft0.6670.0000.3060.6111.0000.012-0.0870.0290.2190.1800.2410.7110.7310.610-0.265
Hbath0.0000.1110.0910.1510.0121.0000.0210.1000.0620.0270.0570.0980.3510.0000.172
Lotsize-0.084-0.0780.047-0.086-0.0870.0211.000-0.171-0.0830.0160.106-0.1730.068-0.2210.605
Nbhd-0.0220.7280.2140.0290.0290.100-0.1711.000-0.0850.8160.1440.0650.5130.051-0.229
Nr_of_rms0.294-0.0420.1810.1990.2190.062-0.083-0.0851.0000.0180.5590.1980.2110.240-0.121
PropType0.0000.0240.3310.0320.1800.0270.0160.8160.0181.0000.0830.1010.8140.0680.042
Sale_price0.1750.0740.0860.1730.2410.0570.1060.1440.5590.0831.0000.1760.1630.0380.043
Stories0.5000.0170.2870.4760.7110.098-0.1730.0650.1980.1010.1761.0000.5830.607-0.282
Style0.0100.2600.3070.3120.7310.3510.0680.5130.2110.8140.1630.5831.0001.0000.431
Units0.5340.0210.4490.5630.6100.000-0.2210.0510.2400.0680.0380.6071.0001.000-0.270
Year_Built-0.170-0.1000.199-0.187-0.2650.1720.605-0.229-0.1210.0420.043-0.2820.431-0.2701.000

Missing values

2024-08-04T10:06:42.682740image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-04T10:06:44.038997image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PropTypeAddressDistrictNbhdStyleExtwallStoriesYear_BuiltNr_of_rmsFin_sqftUnitsBdrmsFbathHbathLotsizeSale_dateSale_price
0Residential3033 N 35TH ST72960AP 1Frame2.0191303476491050402002-0242000
1Residential1908 E WEBSTER PL33170Rm or Rooming HouseFrame2.0189701992422028802002-05145000
2Residential812 N 25TH ST43040Rm or Rooming HouseFrame2.0190702339601031852002-0630000
3Residential959 N 34TH ST42300AP 1Frame2.0189002329441057812002-1066500
4Residential3209 W WELLS ST42300MansionStone2.51891074502760156002002-11150500
5Residential2143 S 11TH ST124120Duplex O/SFrame1.5190602462232050752002-1175000
6Residential1116 N 13TH ST43040Rm or Rooming HouseFrame1.5189002372622077502002-1235000
7Residential3350 W RUSKIN ST114400Cape CodBrick1.0195001149131048002003-0675000
8Residential4826 N 51ST BL11150Cape CodAluminum / Vinyl1.019470994131042002003-0822000
9Residential3706A W SHERIDAN AV11160AP 1Stucco2.0190502938431094802003-12125000
PropTypeAddressDistrictNbhdStyleExtwallStoriesYear_BuiltNr_of_rmsFin_sqftUnitsBdrmsFbathHbathLotsizeSale_dateSale_price
46703Residential5934 S 18TH ST134860RanchAluminum/Vinyl1.01966614211311130902019-072387500
46704Residential2135 W HENRY AV134860RanchBrick1.01962615071311129962019-101600000
46705Residential6145 S 23RD ST134860Cape CodAluminum/Vinyl1.0200461997132172002019-032800000
46706Residential6235 S 26TH ST134860RanchAluminum/Vinyl1.0196351138131153302019-061650000
46707Residential2235 W BRIDGE ST134860RanchAluminum/Vinyl1.0196761516131189902019-052335000
46708Residential2418 W KIMBERLY AV134860Milwaukee BungalowAluminum/Vinyl1.0192861375131183982019-111849000
46709Residential6687 S 19TH ST134920RanchAluminum/Vinyl1.019605981131160002019-061950000
46710Residential6815 S 19TH ST134920RanchAluminum/Vinyl1.0196151110131162402019-071780000
46711Residential1800 W ASPEN ST134920RanchAluminum/Vinyl1.0196161108131178002019-071860000
46712Residential1747 W ASPEN ST134920RanchAluminum/Vinyl1.019645891131165002019-061535000

Duplicate rows

Most frequently occurring

PropTypeAddressDistrictNbhdStyleExtwallStoriesYear_BuiltNr_of_rmsFin_sqftUnitsBdrmsFbathHbathLotsizeSale_dateSale_price# duplicates
26Residential5853 N 74TH ST2980RanchAluminum / Vinyl1.019520969121048002009-0550003
0Residential10939 W CAMERON AV51040RanchBrick1.0196061015131173452021-091500002
1Residential1308 S LAYTON BL84000ColonialFrame2.0192201824141155902009-121010002
2Residential1756 N HI MOUNT BL102580Residence O/SMasonry / Frame2.0191502728152181002017-043350002
3Residential1937 S 5TH ST124120Duplex O/SAluminum / Vinyl1.5191001846262028802016-06630002
4Residential2012 W ORCHARD ST84100Residence O/SAluminum / Vinyl1.0189201523142042002010-05800002
5Residential2021 W PLAINFIELD AV134660RanchBrick1.0196551150131159002019-0115790002
6Residential230 W MARTIN LA134740RanchBrick1.019580990131076502016-091560002
7Residential2554 N 46TH ST152520Milwaukee BungalowAluminum / Vinyl1.0191901517132050002014-12950002
8Residential2636 S 65TH ST114240Cape CodBlock1.0194901406142049002015-071595002